Traditional means for automatic recognition and/or identification of items largely has been limited to barcodes and special symbols. In recent years, such traditional means have been evolving (for instance, in modern two-dimensional and three-dimensional computer vision systems) to include capabilities for improving the recognition, identification, and tracking of whole objects and their parts. Many approaches to object recognition have been proposed and implemented in the past several years in the field of computer vision, varying from basic data filtering (e.g., edge detection) to complex feature-based methods (e.g., Scale-Invariant Feature Transform (SIFT) or Speeded Up Robust Features (SURF)) and multiple transform-domain approaches. The underlying impetus for these approaches is that, different from humans, it is a challenging task for a computer vision system to recognize different objects in images or video sequences, particularly when such objects are translated, rotated, scaled in size, and/or partially obstructed from view. Furthermore, it is generally quite difficult for computer vision systems to provide a reliable and robust degree of confidence for recognition of shapes and objects in images and/or video sequences.